Báo cáo sinh học: "HuMiTar: A sequence-based method for prediction of human microRNA targets" ppt

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Algorithms for Molecular Biology
Open Access
Research
HuMiTar: A sequence-based method for prediction of human
microRNA targets
Jishou Ruan
1
, Hanzhe Chen
1
, Lukasz Kurgan*
2
, Ke Chen
2
, Chunsheng Kang
3

and Peiyu Pu
3
Address:
1
Chern Institute for Mathematics, College of Mathematics and LPMC, Nankai University, Tianjin, PR China,
2
Department of Electrical
and Computer Engineering, University of Alberta, Canada and
3
Neuro-oncology laboratory, General Hospital of the Tianjin Medical University,
Tianjin, PR China
Email: Jishou Ruan - ; Hanzhe Chen - ; Lukasz Kurgan* - ;

Ke Chen - ; Chunsheng Kang - ; Peiyu Pu -
* Corresponding author
Abstract
Background: MicroRNAs (miRs) are small noncoding RNAs that bind to complementary/partially
complementary sites in the 3' untranslated regions of target genes to regulate protein production
of the target transcript and to induce mRNA degradation or mRNA cleavage. The ability to
perform accurate, high-throughput identification of physiologically active miR targets would enable
functional characterization of individual miRs. Current target prediction methods include
traditional approaches that are based on specific base-pairing rules in the miR's seed region and
implementation of cross-species conservation of the target site, and machine learning (ML)
methods that explore patterns that contrast true and false miR-mRNA duplexes. However, in the
case of the traditional methods research shows that some seed region matches that are conserved
are false positives and that some of the experimentally validated target sites are not conserved.
Results: We present HuMiTar, a computational method for identifying common targets of miRs,
which is based on a scoring function that considers base-pairing for both seed and non-seed
positions for human miR-mRNA duplexes. Our design shows that certain non-seed miR
nucleotides, such as 14, 18, 13, 11, and 17, are characterized by a strong bias towards formation of
Watson-Crick pairing. We contrasted HuMiTar with several representative competing methods on
two sets of human miR targets and a set of ten glioblastoma oncogenes. Comparison with the two
best performing traditional methods, PicTar and TargetScanS, and a representative ML method that
considers the non-seed positions, NBmiRTar, shows that HuMiTar predictions include majority of
the predictions of the other three methods. At the same time, the proposed method is also capable
of finding more true positive targets as a trade-off for an increased number of predictions.
Genome-wide predictions show that the proposed method is characterized by 1.99 signal-to-noise
ratio and linear, with respect to the length of the mRNA sequence, computational complexity. The
ROC analysis shows that HuMiTar obtains results comparable with PicTar, which are characterized
by high true positive rates that are coupled with moderate values of false positive rates.
Conclusion: The proposed HuMiTar method constitutes a step towards providing an efficient
model for studying translational gene regulation by miRs.
Published: 22 December 2008

Algorithms for Molecular Biology 2008, 3:16 doi:10.1186/1748-7188-3-16
Received: 3 May 2008
Accepted: 22 December 2008
This article is available from: />© 2008 Ruan et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( />),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Algorithms for Molecular Biology 2008, 3:16 />Page 2 of 12
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Background
MicroRNAs (miRs) are endogenously expressed non-cod-
ing RNAs, which downregulate expression of their target
mRNAs by inhibiting translational initiation or by induc-
ing degradation of mRNA [1]. They are associated with
numerous gene families in multi-cellular species and their
regulatory functions in various biological processes are
widespread [2-14]. The ability to perform accurate, high-
throughput identification of physiologically active miR
targets is one of the enabling factors for functional charac-
terization of individual miRs. This is also true in case on
human miRs, for which only a handful have been experi-
mentally linked to specific functions. The methods for the
prediction of miR targets can be subdivided into two
classes, traditional approaches, which combine several
factors such as sequence complementarity, minimization
of free energy, and cross-species conservation, and
machine learning (ML) methods that exploit statistical
patterns that differentiate between true and false miR-
mRNA duplexes. The former methods aim at finding tar-
get sites for a given miR by scanning 3' untranslated
region (UTR) of the mRNA, while the latter methods clas-

sify a given duplex as true or false.
Current traditional sequence-based target predictors are
based on the presence of a conserved 'seed region' (nucle-
otides 2–7) of exact Watson-Crick complementary base-
pairing between the 3' UTR of the mRNA and the 5' end
of the miR [15,16]. They are based on two principles: (1)
identification of potential miR binding sites according to
specific base-pairing rules in the seed region, and (2)
implementation of cross-species conservation [17].
Recent survey by Sethupathy and colleagues [18] com-
pared five widely used traditional tools for mammalian
target prediction which include DIANA-microT [7],
miRanda [19], TargetScan [3], TargetScanS [11], and Pic-
Tar [10]. They observed that the earlier methods, i.e., Tar-
getScan and DIANA-microT, achieve a relatively low
sensitivity and predict a small number of targets. The
miRanda was shown to provide a substantially better sen-
sitivity as a trade-off for large increase in the total number
of predictions. The two more recent programs, TargetS-
canS and PicTar, have almost identical sensitivity when
compared with miRanda but they predict several thou-
sand fewer miR-mRNA interactions. Another survey that
investigated several traditional predictors including Pic-
Tar, TargetScanS, miRanda, and RNAhybrid [8], concludes
that miRanda and RNAhybrid obtain lower accuracy and
sensitivity when compared with TargetScanS and PicTar
[17]. These conclusions were also confirmed in a recent
study by Huang and colleagues [16]. They show that the
highest quality predictions are obtained by TargetScanS,
closely followed by PicTar, while miRanda and DIANA-

microT were ranked lower. Most recently, Kuhn and col-
leagues suggest use of PictTar, TargetScanS, and PicTar to
perform computational prediction of miR targets [20].
Based on the above, our experimental section includes
three representative miR target prediction methods, Tar-
getScanS, PicTar, and Diana-MicroT. The first two were
selected based on their favorable performance, while pre-
dictions of Diana-MicroT were used as a point of refer-
ence, i.e., representative early generation program
characterized by a relatively low sensitivity.
Recent research resulted in development of several ML
methods. These methods usually filter predictions pro-
vided by the traditional predictors. Their main drawback
is that they filter targets by using a predefined and rela-
tively small number of false targets, i.e., they do not scan
the mRNA sequence but instead they simulate that by
using a small set of negatives (false targets). For instance,
a method by Yan and colleagues filters miRanda's predic-
tions based on 48 positive and 16 negative sites [21]. A
more recent, NBmiRTar method, which also filters predic-
tions of miRanda, applies 225 true miR targets, 38 con-
firmed negative sites, and up to 5000 of artificially
generated negative sites [22]. The most recent method is
based on binding matrix technique, in which the informa-
tion concerning both the miRNA sequence and a set of
experimentally validated targets is used to perform predic-
tions [23]. The main drawback of this approach is the
necessity of providing a set of validated targets which is
not required in case of the proposed and the abovemen-
tioned sequence-based prediction methods. At the same

time, we note that the ML methods establish the predic-
tion model based on information concerning both the
seed and the non-seed positions, which is also exploited
in our research. To this end, we include NBmiRTar
method in our experimental section.
We aim at developing a novel, traditional prediction
method, named HuMiTar, which addresses some of the
drawbacks of the existing seed-based methods. Although
the existing methods strongly emphasize the seed-region
complementarity and the cross-species conservation, as
many as 40% of seed region matches that are conserved
between human and chicken are false positives [11], and
imperfect pairing is shown to occur in the seed region
[24]. Another recent study indicates that almost 30% of
the experimentally validated target sites are not conserved,
motivating the development of alternative computational
methods [18]. Although relaxation of the conservation
results in higher sensitivity, it also leads to higher false
positive rates, which in turn results in necessity of per-
forming extensive laboratory verification on the predicted
interactions [16]. A recently proposed solution to increase
quality of traditional predictors is based on filtering pre-
dictions of sequence-based methods using profiling of
miR and mRNA expressions [16]. We propose an alterna-
tive approach, in which instead of filtering results of exist-
ing sequence-based methods (as done by the ML
methods), we develop a novel sequence-based design that
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aims at improving true positive rates. We collected statis-

tical information using a design dataset of 66 human miR-
mRNA duplexes that were published in TarBase [25]
before 2006, see Table 1 [see Additional file 1]. HuMiTar
incorporates two main components which are designed
based on a quantitative analysis of these duplexes: (1) a
novel composite scoring function that quantifies strength
of miR-mRNA binding and which incorporates informa-
tion about base-pairing for both seed and non-seed posi-
tions; and (2) a 2D-coding method that finds potential
targets in 3'UTR which are next scored and filtered via the
scoring function. Improved prediction quality of the pro-
posed method is a result of a careful design and optimiza-
tion that is focused on human targets. The motivation to
choose human targets comes from two facts: (1) target
prediction for plants is easier than for animals [26,27];
and (2) identification of miR targets is critical to advanc-
ing understanding of human diseases, such as cancer, aris-
ing from misregulation of gene expression caused by miRs
[28]. At the same time, to date, a relatively small number
of target genes in various tumors was experimentally iden-
tified for some miRs [29].
Results and discussion
Datasets and experimental setup
Dataset used to validate and compare the proposed pre-
diction method are summarized in Table 1. The following
empirical tests were performed:
1. Comparison of sensitivity – number of predicted targets
trade-off. HuMiTar, PicTar, DIANA-MicroT, TargetScanS,
and NBmiRTar were compared on the design set and the
independent set.

2. Comparison of the overlap of predictions. HuMiTar is com-
pared with the best-performing traditional method PicTar
and TargetScanS and ML method NBmiRTar on the GO
set. We also include Western blots which are used to verify
correctness of some of the HuMiTar predictions.
3. Evaluation and comparison of sensitivity/specificity trade-off
based on ROC (receiver operating characteristic) analysis.
The predictions of HuMiTar on the interactions set were
compared with results of five competing predictions
methods reported in [30].
4. Predictions on p53. HuMiTar predicted a set of miRs that
target p53, some of which were independently verified in
[31].
5. Genome-wide target prediction. HuMiTar was applied to
perform genome wide predictions for 16 different species.
We also estimate the signal-to-noise ratio based on predic-
tions for human 3' UTRs and using the procedure intro-
duced to validate PicTar [10]. This ratio and the analysis
presented in test 1 are performed to estimate specificity of
the proposed method; similar evaluation for the tradi-
tional methods was done in [3,10,11,17,18]. Finally, we
also estimate the computational complexity of the pro-
posed method.
Table 1: Datasets.
Dataset name Dataset details Dataset goal
Design set 66 human miR-mRNA duplexes published in TarBase
before 2006, see Table 1 [see Additional file 1]; this set
includes 29 miRs and 36 genes.
Design of the proposed prediction method.
Evaluation and comparison of sensitivity – #

predictions trade-off and overlap between the
predictions of different methods.
Independent set 39 human miRs that were published in TarBase between
January 2006 and June 2007; this set includes 20 miRs
and 26 genes.
Evaluation and comparison of sensitivity – #
predictions trade-off and overlap between the
predictions of different methods.
Interactions set 190 miR-mRNA interactions pairs experimentally tested
in Drophilia. The dataset was taken from [30].
Evaluation and comparison of the specificity/
sensitivity trade-off. The ROC curves and AUC
values were compared with results of five competing
methods reported in [30].
GO (glioblastoma oncogenes) set Ten glioblastoma oncogenes. The choice is motivated by
our expertise in profiling glioblastoma. Although our
goal was to compare predictions on 17 glioblastoma
oncogenes, only 10 of them could be found in PicTar
database. The oncogenes and the associated 328 miRs
are given in Tables 2 and 3 [see Additional file 1],
respectively.
Comparison of the sensitivity, number of predicted
targets and overlap between the predictions of
different methods.
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Comparison of sensitivity – number of predicted targets
trade-off
Detailed results for the five prediction methods (HuMi-
Tar, PicTar, DIANA-MicroT, TargetScanS, and NBmiRTar)

and each of the miRs in the design and independent sets
are listed in Tables 4 and 5 [see Additional file 1], respec-
tively. The predictions are summarized in Figure 1. Fol-
lowing the analysis performed in [18], the Figure shows
sensitivity (number of predicted published targets divided
by total number of published targets) against the total
number of predictions. We show results for each of the
five methods, and also when combining (using union)
predictions of HuMiTar with each of the competing
method, as well as for the union of the four competing
methods. This allows not only to analyze sensitivity-spe-
cificity trade-off, as defined in [18], but also to investigate
complementarity between predictions of different meth-
ods. We note that increased sensitivity comes at a price of
the increased number of predictions. We also note that
TargteScanS and PicTar have comparable sensitivity, while
the sensitivity of DIANA-MicroT is much lower, which
agrees with the conclusions from [18]. We observe that:
(1) HuMiTar provides the highest sensitivity among the
five predictors as a trade-off for a moderate increase of the
number of predicted targets, i.e., 67 vs. 59/47 targets were
predicted by PicTar/TargetScanS for the design set and 48
vs. 37/20 were predicted by PicTar/TargetScanS for the
independent set; (2) DIANA-MicroT is shown to provide
the lowest sensitivity and low number of predictions; (3)
TargetScanS provides the second best sensitivity with a rel-
atively low number of predicted targets; (4) NBmiRTar
obtains results comparable to PicTar on the design set and
a relatively low sensitivity on the independent set; (5)
addition of predictions of competing methods to predic-

tions of HuMiTar results in small or no improvement in
sensitivity while it increases the total number of predic-
tions; (6) union of the competing four predictors on the
independent set shows relatively low sensitivity with sim-
ilar number of predicted targets when compared with
HuMiTar. The last two findings indicate that HuMiTar is
capable of providing additional true positive predictions
as a trade-off for a moderate increase in the number of
predicted targets. The largest number of 23 (design set)
and 22 (independent set) unpublished targets, i.e., targets
found for some of the miRs from the design/independent
set that are not published in the TarBase, was found by
PicTar. These targets may correspond to biologically
meaningful sites or may constitute false positive predic-
tions. The five methods predict relatively low number of
unpublished targets, especially in the context of the gener-
ated number of true positives and the fact that PicTar was
previously shown to provide relatively low false positive
rates [10].
Following, we concentrate on the results on the independ-
ent set since this set was not used to design the proposed
method and thus it allows for an unbiased analysis. A
recent study by Nielsen and colleagues reveals several miR
targeting determinants [32]. They concern patterns out-
side of the seed and include presence of adenosine oppo-
site miR base 1 and of adenosine or uridine opposite miR
base 9. We applied both of these determinants on the set
of 39 duplexes, and found 10 matches, i.e., 10 duplexes
satisfy both of the determinants. All of these 10 duplexes
were correctly predicted by HuMiTar, while 8 were pre-

dicted by TargetScanS, 6 by PicTar, 5 by NBmiRTar, and
none by DIANA-MicroT. When considering 13 out of 39
cases for which the adenosine or uridine was opposite
miR base 9, all of them were correctly classified by HuMi-
Tar, and 10, 8, 6, and 0 by TargerScanS, PicTar, NBmiRTar,
and DIANA-MicroT, respectively. Finally, for 19 duplexes
in which the adenosine was opposite miR base 1, 16 of
them were found by HuMiTar, 12 by TargerScanS, 10 by
PicTar, 7 by NBmiRTar, and none by DIANA-MicroT. This
provides an independent validation of the improvements
provided by the HuMiTar, which uses scoring function
that considers base-pairing outside of the seed, in contrast
to the traditional methods that are based on the base-pair-
ing rules only in the seed region.
Comparison of the overlap of predictions
328 human miRs were predicted on the GO set with Pic-
Tar, TargetScanS, NBmiRTar, and HuMiTar to analyze
overlap between predictions of different methods. The
results are summarized in Figure 2. The detailed results
(including values for individual oncogenes) that compare
HuMiTar with PicTar, TargetScanS, and NBmiRTar are
provided in Tables 6, 7, and 8 [see Additional file 1],
respectively. Since PicTar's database does not include
some of the miRs considered in this test, they were
excluded from the comparison with this method, which
results in a lower total number of PicTar's predictions. Fol-
lowing we compare the predictions of HuMiTar with each
of the three competing methods.
HuMiTar predicts 97% of the targets that were predicted
by PicTar while only 4 targets were predicted exclusively

by PicTar. At the same time, HuMiTar finds numerous
extra targets that were not predicted by PicTar. Among
them, 646 and 442 extra targets were predicted for miRs
that are and that are not included in the PicTar's database,
respectively. Although the results show that HiMiTar gen-
erates larger number of predictions that cover virtually all
predictions made by PicTar, these additional prediction
could constitute either true or false positives. Since it
would infeasible to verify correctness of the entire set of
1088 additional predictions provided by HuMiTar, we
concentrate our efforts on a specific target, Septin7, due to
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Summary of prediction results of HuMiTar (HT), PicTar (PT), DIANA-MicroT (DM), TargetScanS (TS), and NBmiRTar (NT). Panel A gives results for the design set of 66 miR-mRNA duplexes. Panel B gives results for the independent set of 39 miR-mRNA duplexesFigure 1
Summary of prediction results of HuMiTar (HT), PicTar (PT), DIANA-MicroT (DM), TargetScanS (TS), and
NBmiRTar (NT). Panel A gives results for the design set of 66 miR-mRNA duplexes. Panel B gives results for
the independent set of 39 miR-mRNA duplexes. The hollow circles show results of individual methods, hollow triangles
show results of union between HuMiTar and one competing method, and cross corresponds to union of the four competing
methods.
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Comparison of the number of predicted targets and their overlap for predictions on the GO set. Panel A shows the number of targets predicted by the three competing methods including PicTar, TargetScanS, and NBmiRTar. The white area represents targets that overlap with predictions of HuMiTar and the black area shows the remaining targets. Panel B shows the number of targets predicted by HuMiTar. The white area shows overlap with predictions of a competing method indicated at the x-axis, and the black area shows predictions specific to HuMiTarFigure 2
Comparison of the number of predicted targets and their overlap for predictions on the GO set. Panel A
shows the number of targets predicted by the three competing methods including PicTar, TargetScanS, and
NBmiRTar. The white area represents targets that overlap with predictions of HuMiTar and the black area
shows the remaining targets. Panel B shows the number of targets predicted by HuMiTar. The white area
shows overlap with predictions of a competing method indicated at the x-axis, and the black area shows pre-
dictions specific to HuMiTar. In the case of PicTar, the predictions are reduced to a set of miRs that are included in the Pic-
Tar's database />.
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our existing wet-lab expertise. The test considers three sets
of miRs:
Set 1
5 miRs from the 18 targets that were predicted by both
HuMiTar and PicTar, i.e. miR-19a, miR-127, miR-141,
miR-182, and miR183. This set of used to investigate
whether the common results in fact concern true posi-
tives.
Set 2
5 miRs from the 23 targets that were predicted only by
HuMiTar and which are not included in the PicTar's data-
base, i.e. miR-202, miR-248, miR-412, miR-453, and miR-
450. This set concerns miRs that have not been predicted
by PicTar, i.e. they were predicted only with the use of
HuMiTar.
Set 3
11 miRs from the 34 extra targets on Septin7, which were
found only by HuMiTar although these miRs are included
in PicTar's database, i.e. miR-148, miR-106b, miR-134,
miR-106, miR-144, miR-151, miR-384, miR-101, miR-
142, miR-129, and miR-126. This set is of particular inter-
est (and thus it is larger), since it concerns targets that were
not predicted by PicTar, but which were predicted by
HuMiTar.
We run a Western blot according to the following proce-
dure. The human glioblastoma cell line U251 was
obtained from China Academia Sinica cell repository in
Shanghai, China. All cell lines were grown in Dulbecco's
modified Eagle's medium (DMEM) (Gibco, USA) supple-

mented with 10% fetal bovine serum (Gibco, USA), 2 mM
glutamine (Sigma, USA), 100 units of penicillin/ml
(Sigma, USA), and 100 μg of streptomycin/ml (Sigma,
USA), incubated at 37°C with 5% CO2, and sub-cultured
every 2~3 days. The antisense oligonucleotides of the pre-
scanned miRNAs were chemically synthesized by GeneP-
harma (Shanghai, China) and were transfected into U251
cells by Oligofectamine (Invitrogen, USA) according to
the manufactures' protocol. Parental and transfected cells
were washed with ice-cold phosphate-buffered saline
(PBS) three times. The cells were then solubilized in 1%
Nonidet P-40 lysis buffer (20 mM Tris, pH 8.0, 137 mM
NaCl, 1% Nonidet P-40, 10% glycerol, 1 mM CaCl
2
, 1
mM MgCl
2
, 1 mM phenylmethylsulfonyl fluoride, 1 mM
sodium fluoride, 1 mM sodium orthovanadate, and a pro-
tease inhibitor mixture). Homogenates were clarified by
centrifugation at 20,000 ×g for 15 minutes at 4°C and
protein concentrations were determined by a bicin-
choninic acid protein assay kit (Pierce, USA). Equal
amounts of lysates were subjected to SDS-PAGE on 8%
SDS-acrylamide gel. Separate proteins were transferred to
PVDF membranes (Millipore, USA) and incubated with
primary antibody against Septin-7 (Santa Cruz, USA), fol-
lowed by incubation with HRP-conjugated secondary
antibody (Zymed, USA). The specific protein was detected
by using a SuperSignal protein detection kit (Pierce, USA).

The membrane was stripped and re-probed with antibody
against β-actin (Santa Cruz, USA).
The Western blot given in Figure 3 shows that:
- In Set 1, up-regulation is shown for miR-19a (position
5), miR-183 (position 9), and miR-141 (position 10); we
also observe that miR-182 (position 3) is likely to be a
true positive. The experiment shows that miR-127 does
Western blots for selected 21 miRs and Septin7Figure 3
Western blots for selected 21 miRs and Septin7. The Septin7 expression levels were measured (left to right) for (1)
control sample, (2) miR-127, (3) miR-182, (4) miR-412, (5) miR-19a, (6) miR-453, (7) miR-448, (8) miR-450, (9) miR-183, (10)
miR-141, (11) miR-202, (12) miR-148, (13) miR-106b, (14) miR-134, (15) miR-106, (16) miR-144, (17) miR-151, (18) miR-384,
(19) miR-101, (20) miR-142, (21) miR-129 and (22) miR-126. The position 1 is the control sample; positions 2 to 11 inclusive
concern 10 miRs for which predictions were obtained either by both PicTar and HiMiTar (positions 2, 3, 5, 9, and 10) or only
by HuMiTar while these MiR were not included in the PicTar's database (positions 4, 6, 7, 8, and 11); positions 12 to 22 con-
cern 11 miRs which were predicted by HuMiTar and which were included in the PicTar's database. We note that our analysis
lacks results on the mutant targets that would strengthen the claim that the activation results of up-regulation of the predicted
miRs. Due to limited resources and since the goal of this work is to present a new in-silico prediction method rather than to
investigate whether miRs can up-regulate translation, we note that our conclusions concerning the up-regulation should not be
considered as the primary outcome of this work.
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not affect the expression levels of Septin7, which may sug-
gest that this is a false positive prediction. To summarize,
4(3) out of 5 of the predictions in Set 1 are shown to be
true positives.
- In Set 2, miR-448 (position 7), miR-450 (position 8),
and miR-202 (position 11) show up-regulation; miR-453
(position 6) is a borderline case, although the Western
blot suggests that it could be classified as a true positive.
Finally, miR-412 has no impact on the Septin7 expres-

sion, and thus it should be considered as a false positive.
As a result, 4(3) out of 5 predictions in this set are true
positives.
- In Set 3, the Western blots indicate that all 11 miRs
(positions 12 to 22 in Figure 3) target Septin7 and thus
they constitute true positive predictions.
Overall, the experiment indicates that for the considered
set of miRs, HuMiTar obtains about 80% sensitivity when
predicting targets on Septin7. The reported up-regulation
is consistent with recent research that also indicates that
miRs can up-regulate the translation [33,34]. Although
the above results cannot be generalized to other targets,
they indicate that predictions generated by the proposed
method are characterized by favorable sensitivity when
compared with PicTar.
Among the TargetScanS predictions, 91% were also pre-
dicted by HuMiTar and the remaining 9%, i.e., 68 targets,
were not included in the output of HuMiTar. At the same
time, the proposed method provides 562 additional pre-
dictions. Similarly as in case of comparison with PicTar,
we probe the sensitivity of both prediction methods based
on targets predicted for Septin7. Analysis of 39 miRs that
were predicted exclusively by HuMiTar shows that 11 of
them (miR-101, miR-126, miR-129, miR-134, miR-144,
miR-151, miR-202, miR-384, miR-412, miR-450, miR-
453) are included in the Western blot on Figure 3. Among
them, nine are true positives, miR-453 is a borderline case,
and miR-412 is a false positive. Although our limited
resources prohibit more extensive experimental analysis,
the above analysis suggests that additional predictions

provided by HuMiTar include true positives.
Finally, although the overlap between the predictions of
HuMiTar and NBmiRTar is the smallest among the three
competing methods, it still constitutes over a half (56%)
of the NBmiRTar's predictions. The HuMiTar provides
949 predictions which are not included in the output of
NBmiRTar, while 213 predictions are exclusive to NBmiR-
Tar.
Overall, the test on the GO set shows that predictions of
HuMiTar overlap with the predictions of the competing
methods. In particular, we note that the HuMiTar's out-
puts cover almost all predictions of PicTar and majority of
predictions of the other two methods. At the same time,
our predictions also include novel targets that could cor-
respond to biologically meaningful sites.
Evaluation and comparison of sensitivity/specificity trade-
off
We use the interactions set from [30] to investigate and
compare the trade-off between sensitivity and specificity
of HuMiTar and several existing methods. This test differs
from the other tests shown in this contribution as it sim-
plifies this prediction problem to finding whether a given
miR interacts with a given mRNA, i.e., the prediction of
the exact location of the target site is ignored. The miR-
mRNA pairs from the interactions set are reported in a
binary format, as being either functional or non-func-
tional, and we use the area under the ROC curve (AUC)
measure to evaluate the sensitivity and specificity of our
prediction method. We predict all potential sites in the
3'UTR regions for a given miR and we use the maximal

score computed by the scoring function to decide whether
the interaction occurs. The ROC curve, see Figure 4, shows
the trade-off between the true positive (TP) rate (number
of correct predictions of the functional miR-mRNA pairs
divided by the total number of functional pairs) and the
false positive (FP) rate (number of miR-mRNA pairs that
were incorrectly predicted as functional divided by the
total number of non-functional pairs) obtained by thresh-
olding the scores. HuMiTar is compared against five other
predictions methods, PicTar, miRanda, predictor pro-
posed by Stark and colleagues in [35], STarMir [36], and
PITA [30], which were reported in [30]. The proposed
method achieves AUC equal 0.70, which is better than the
results of STarMir and MiRanda and comparable to results
of PicTar and the method by Stark et al. We emphasize
that some of these methods use information concerning
conservation of the sites in related species, while the only
inputs for HuMiTar are the mRNA and MiR sequences.
Although HuMiTar is outperformed by PITA, we note that
the latter method uses secondary structure of the target to
perform the predictions while the proposed method uses
only the sequence. We observe that HuMiTar obtains
higher TP rates when assuming moderate values of FP
rates, i.e., it correctly predicts more functional duplexes
when assuming a larger number of false positives. When
using the default values of the scoring threshold, which
equals 70, the proposed method obtains TP rate = 0.85
and FP rate = 0.44. This shows that HuMiTar predicts sig-
nificant majority of the actual duplexes while achieving
acceptable FP rate when compared with the other consid-

ered methods at this TP rate. The only method that
obtains such high TP rate is PITA and its corresponding FP
rate equals 0.41 (for both versions with and without
flanking nucleotides).
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Predictions on p53
HuMiTar was applied to predict targets on p53, which is
one of the most important tumor suppressor proteins. We
note that PicTar does not report predictions for this target.
Our method predicted total of 147 miRs that target this
protein, and 15 of them, see Table 2, coincide with micro-
array-based results in [31]. We are currently unable to con-
firm or refute the remaining predictions.
Genome-wide target prediction
Table 3 shows an overview of predictions on 39,215
3'UTR sequences in human genome and on 15 other
genomes. The table shows the number of miRs used to
predict sites for each species, the total number of targets
predicted by HuMiTar, and the average number of pre-
dicted targets per one miR. Our predictions indicate that,
on average, the number of targets for a single miR across a
genome equals 9,613. We also observe that the number of
predicted targets per miR is similar between different
genomes.
One of the accepted ways of assessing the statistical signif-
icance of predicted targets, which was performed in
[3,10,11], is based on using random miR sequences
('mock' miRs) as controls [17]. The motivation is that the
mock sequences are unlikely to be biologically relevant

and thus observing the ratio between the number of pre-
dictions for real miRs and for the mock miRs would indi-
cate how many of the predictions for real miRs are indeed
biologically relevant. The ratio of 'real' versus 'mock' pre-
dictions is provided as an estimate of the signal-to-noise
ratio (SNR) of the target predictions [17]. The HuMiTar's
SNR was evaluated using the set of 58 miRs and the rand-
omization procedure to generate the mock miRs that were
originally used to estimate SNR for PicTar [10]. The SNR
of HuMiTar for the 58 real/mock miRs in the entire
human 3'UTR set equals 1.99. PicTar's SNR was estimated
to equal 1.8 when considering conservation using human,
chimpanzee and mouse genomes, and 2.3 and 3.6 when
dog and chicken genomes were added, respectively [10].
In case of TargetScanS the ratio was estimated to be 2.4
and 3.8 when considering all predictions and when con-
sidering only the positions conserved in all five genomes,
respectively [11]. We note that the latter improved SNR
was also accompanied by a 51% loss in sensitivity [11].
The SNR of the proposed method, which does not imple-
ment cross-species conservation, is comparable with the
ratio of PicTar that was computed when the cross-species
conservation was limited to human, chimpanzee and
mouse genomes, and to the SNR of TargetScanS when
conservation was not included. We anticipate that the
SNR would increase if we would incorporate the cross-
species conservation to filter our predictions.
Computational complexity
The asymptotic computational complexity of HuMiTar is
O(m

2
n) where m is the length of miR sequence and n is the
ROC curves and the corresponding AUC scores that quan-tify sensitivity and specificity of different miR target predic-tors on the interactions setFigure 4
ROC curves and the corresponding AUC scores that
quantify sensitivity and specificity of different miR
target predictors on the interactions set. The results
include five existing prediction methods, PicTar [10],
miRanda [19], method by Stark and colleagues [35], STarMir
[36], and PITA [30]. The latter method includes two ver-
sions, one requiring unpairing of only target-site nucleotides
(PITA no flank) and another that also requires unpairing of 3
and 15 flanking nucleotides upstream and downstream of the
target site (PITA 3/15 flank), respectively. For each predic-
tion method, the targets were sorted by score and the FP
rates (x axis) and TP rates (y axis) were plotted for each pos-
sible score prediction threshold. The area under the curve
(AUC) for each method is shown in the figure legend. The
AUC is computed by extending each plot to the upper right
corner as in [30]. The results obtained by a random sorting
of the targets are shown using a thin dashed line. The ROC
curves and AUC values of PITA, STARK, PicTar, miRanda,
and STarMir were taken from [30].
Table 2: List of 15 miRs that were predicted by HuMiTar to target p53 and which were confirmed by Xi and colleagues (Xi et al.,
2006).
hsa-let-7a hsa-miR-296 hsa-miR-125b hsa-miR-183 hsa-miR-19b
hsa-miR-30b hsa-miR-30c hsa-miR-30a-5p hsa-miR-30d hsa-miR-27a
hsa-miR-103 hsa-miR-107 hsa-miR-92 hsa-miR-10a hsa-miR-326
Algorithms for Molecular Biology 2008, 3:16 />Page 10 of 12
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length of the mRNA. This dominant factor contributing to

the overall complexity is computation of 2D-coding that
is used to perform initial screening of the mRNA. We note
that since m is a small constant, the proposed method is
characterized by a linear complexity with respect to the
length of the mRNA sequence. We also performed experi-
mental evaluation of the execution time. Using the set of
39,215 human 3'UTR sequences (the total lengths of these
sequences equals 3.62e+07) the search for miR-21 targets
takes 1,488 seconds using a desktop computer. We also
computed execution times for ten randomly drawn miRs
shown in Table 9 [see Additional file 1]. The targets were
predicted on average in 1,451 seconds (about 24 minutes)
per one miR, which shows that the proposed method can
be applied on the genomic scale.
Conclusion
The prediction of animal miR targets is an open and diffi-
cult problem in spite of several years of the existing
research. HuMiTar, which is a prediction method
designed based on human miR targets, is shown to pro-
vide predictions characterized by favorable sensitivity,
which comes at a price of an increased number of predic-
tions. The HuMiTar's predictions cover majority of the
predictions of the best-performing competing methods
such as PicTar, TargetScanS, and NBmiRTar. HuMiTar has
good computational efficiency and comparable signal-to-
noise ratio when compared with TargetScanS and PicTar.
ROC analysis shows that HuMiTar provides predictions of
quality that is comparable with the quality of PicTar,
while our predictions are characterized by high true posi-
tive rates and moderate values of false positive rates. Our

prediction method constitutes a step towards providing
an efficient computational model for studying transla-
tional gene regulation by microRNAs. Our future work
will concentrate of relaxation of the base-pairing require-
ments in the seed region to accommodate for miR-mRNA
with the imperfect pairing and inclusion of information
based on the stacked pairs and unpaired regions.
Table 3: Summary of genome-wide predictions with HuMiTar.
Species type # of miRs used Total # of targets predicted with HuMitar Average number of targets per miR
Anopheles gambiae 37 324050 8758
Bos taurus 125 1234929 9879
Caenorhabditis briggsae 93 759339 8165
Caenorhabditis elegan 134 1035742 7729
Drosophila melanogaster 78 728121 9335
Drosophila pseudoobscura 68 653553 9611
Fugu rubripes 109 1083787 9943
Gallus gallus 133 1302995 9797
Homo sapiens 471 4591244 9748
Macaca mulatta 70 738781 10554
Monodelphis domestica 100 1038582 10386
Mus musculus 380 3470973 9134
Pan troglodytes 80 843686 10546
Rattus norvegicus 238 2379080 9996
Tetraodon nigroviridis 109 1090377 10003
Xenopus tropicalis 160 1634721 10217
average 149 1431873 9613
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Methods
HuMiTar works in two steps: (1) a 2D-coding method

finds candidate targets by scanning 3'UTR of a given
mRNA; and (2) the selected candidate targets are filtered
using a composite scoring function.
Scoring function
The scoring function was designed based on statistical
analysis of 66 miR-mRNAs duplexes from the design set.
Based on analysis of several recent studies [2-14,24], the
miR sequence was divided into four regions: (1) position
1; (2) seed region (positions 2 to 8); (3) region 1 (posi-
tions 9 to 13); and (4) region 2 (positions 14 to 20). We
computed conditional (assuming that they concern only
the actual sites) and unconditional (assuming any other
position in the mRNA using a sliding window) frequen-
cies of the nucleotide pairs formed between miR's seed
region, region 1, and region 2 and the corresponding 3'
UTR of the mRNA. These values were used to compute
affinity of each pair to form a bond between miR and
mRNA; the affinity values are shown in Table 11 [see
Additional file 1]. The differences in the obtained affinity
values of the same pairs for different regions show that the
occurrence of the corresponding pairs in a considered can-
didate complex should be weighed accordingly. We apply
the principles of balance of moments to compute the
weight values. The underlying interpretation is that the
high affinity to bind in the seed region between a given
miR and mRNA fragment should be balanced by suffi-
ciently large affinity to bind in the non-seed regions
(regions 1 and 2). We assume that the sum of moments
generated by positions in the seed region should be
greater than the sum of moment of the positions in non-

seed regions. This problem is formulated and solved, i.e.,
the corresponding scoring function that optimizes the
balance between binding in the seed and the non-seed
regions is parameterized, using a standard linear program-
ming model. The parameterization shows that the forma-
tion of complementary pairs for positions 9, 10, 12, 15,
16, 19 and 20 is less "important" than for the positions
11, 13, 14, 17 and 18. This result can be validated against
a recent study that investigated Watson-Crick pairing for
contiguous nucleotides. This study concluded that when
excluding the seed, positions 13–16 have the strongest
preference for the complementary pairing [37]. Although
we consider each position individually, while the other
study analyzed multimers, we also found that positions
13 and 14 have strong tendency to form complementary
pairs. Our prediction model considers existence of two
opposing forces: positive effect corresponding to the for-
mation of complementary base pairs (which is quantified
by the reward function), and negative effect due to the exist-
ence of non-complementary base pairs (quantified by the
penalty function). The scoring function is defined as a dif-
ference between the values produced by the reward and
the cost functions.
2D-coding method
The 3' UTR of mRNA is scanned using a sliding window.
The basic principle of the 2D-coding is to scan an mRNA
segment starting at a given position (using the sliding win-
dow) by finding stretches (segments) of complementary
base pairs, which are denoted by A
i

where i = 1, 2, , 5. We
start with finding the first segment, denoted by A
1
, in the
miR's seed region, and then continue along the miR's
sequence to find the subsequent segments. Given that a
sufficient number of such segments can be found, which
depends on several parameters explained in the [Addi-
tional file 1], the 2D-coding will output a candidate com-
plex. The procedure will stop after finding A
5
since no
more then five complementary segments were found
when scanning duplexes in the design set.
The high-level pseudo-code of HuMiTar method is shown
in Figure 5. Detailed description of the algorithm includ-
ing pseudo-code of the 2D-coding method can be found
in the [Additional file 1].
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
JR contributed to the conception and the design of the
project and the prediction method, contributed to the
design of the experimental study and evaluation of the
results, drafted and corrected the original and the revised
manuscripts, and coordinated the project. HC contributed
to the design of the prediction method and the experi-
mental study, helped with the preparation of the datasets,
performed computations, generated the prediction model
and the experimental results, and helped with evaluation

of the results. LK contributed to the design of the predic-
tion method and the experimental study, evaluation of
the results, drafted and corrected the original and the
revised manuscripts, prepared the revisions, and coordi-
nated the project. KC contributed to the design of the
project and evaluation of the results. CK and PP contrib-
uted to the conception and the design of the project,
helped with the design of the experimental study and eval-
Pseudo-code of the MuMiTar algorithmFigure 5
Pseudo-code of the MuMiTar algorithm.
Algorithms for Molecular Biology 2008, 3:16 />Page 12 of 12
(page number not for citation purposes)
uation of the results, and performed and interpreted West-
ern blots. All authors read and approved all versions of the
manuscript.
Additional material
Acknowledgements
J.R. thanks NSFC (No 10671100), Liuhui Center for applied mathematics,
the joint program of Tianjin and Nankai Universities, and China-Canada
interchange project administered by MITACS. This work was supported in
part by the Discovery grant to L.K. from NSERC Canada. K.C. was sup-
ported by a scholarship sponsored by Alberta Ingenuity and iCORE. C.K.
and P.P. would like also to thank NSFC (No 10671100).
The authors would like to thank Michael Kertesz for providing dataset and
experimental results for the ROC analysis.
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Additional File 1
Supplement for article entitled "HuMiTar: A sequence-based method
for prediction of human microRNA targets". The file provides detailed
description of the HuMiTar algorithm, 13 supplementary tables that give
detailed test results and information that supports the detailed description
of the algorithm, and 4 supplementary figures that assist in explaining the
algorithm.
Click here for file
[ />7188-3-16-S1.pdf]